Methods of improving performance of automotive intersection turn assist features

ABSTRACT

A system and method for warning a vehicle driver of a potential collision when turning left or right at or near an intersection, where the system and method provide additional analysis to limit false positive and false negative warnings based on specialized circumstances. The method includes determining if the host vehicle is likely to turn at or near the intersection, and obtaining speed, velocity and position data of the host vehicle and any relevant remote vehicles. The method determines a predicted path of the host vehicle and the remote vehicles based on the speed, velocity and position data, and issues a warning to a driver of the host vehicle if the host vehicle and one of the remote vehicles may collide based on the predicted paths. If the host vehicle is in a specialized circumstance, the method provides additional collision analysis to reduce false positive warnings and/or false negative warnings.

BACKGROUND OF THE INVENTION

Field of the Invention

This invention relates generally to a system and method for warning avehicle driver of a host vehicle of a possible collision with othervehicles when turning and, more particularly, to a system and method forwarning a vehicle driver of a host vehicle of a possible collision withother vehicles when turning, for example, at an intersection thatincludes providing additional analysis to limit false positive and falsenegative warnings based on specialized circumstances.

Discussion of the Related Art

Object detection systems and collision avoidance systems are becomingincreasingly common on modern vehicles. Object detection systems canprovide a warning to a driver about an object in the path of a movinghost vehicle. The warning can be a visual indication on the vehiclesinstrument panel or in a head-up display (HUD), and/or can be an audiowarning such as chimes or other feedback device, such as haptic seat.Object detection systems can also provide input to active vehiclesystems, such as adaptive cruise control systems, which control vehiclespeed to maintain the appropriate longitudinal spacing to a leadingvehicle, and rear cross traffic avoidance systems, which can provideboth warnings and automatic braking to avoid a collision with an objectbehind the host vehicle when the host vehicle is backing up.

Active safety technology employing object detection systems is currentlybecoming a major area of research in the automotive industry. Advancesin sensor and actuator technologies have enabled the development ofdriver assistance systems (DAS) to prevent road accidents, especiallythose caused by driver mistakes or inattention. Several types of DAS,such as anti-lock braking system (ABS), electronic stability control(ESC), adaptive cruise control (ACC), lane departure warning (LDW)system, lane change assist (LCA), forward collision alert (FCA), andlane keeping assist (LKA), are already in production vehicles. Collisionimminent braking is an effective way of avoiding or mitigating acollision by applying the vehicle brakes. Collision avoidance systemsmay also provide steering commands that cause the host vehicle to followa calculated steering path to provide the vehicle steering to avoid acollision when braking alone can only mitigate the collision.

The object detection sensors for these types of systems may use any of anumber of technologies, such as short range radar, long range radar,cameras with image processing, laser or Lidar, ultrasound, etc. Theobject detection sensors detect vehicles and other objects in the pathof a host vehicle. In many vehicles, the object detection sensors areintegrated directly into the front bumper or other fascia of thevehicle, but other mounting locations are available.

Radar and lidar sensors that may be employed on vehicles to detectobjects around the vehicle and provide a range to and orientation ofthose objects provide reflections from the objects as multiple scanpoints that combine as a point cloud (cluster) range map, where aseparate scan point is typically provided for every ½° across thehorizontal field-of-view of the sensor. These scan points also provide areflectivity measure of the target surface in the form of intensity inaddition to the range and azimuth angle values, and therefore, if atarget vehicle or other object is detected in front of the host vehicle,there may be multiple scan points that are returned that identify thesurface reflectivity, distance and azimuth angle of the target vehiclefrom the subject vehicle. By providing a cluster of scan return points,objects having various and arbitrary shapes, such as trucks, trailers,bicycle, pedestrian, guard rail, K-barrier, etc., can be more readilydetected, where the bigger and/or closer the object to the host vehiclethe more scan points are provided.

Cameras on a vehicle may provide back-up assistance, take images of thevehicle driver to determine driver drowsiness or attentiveness, provideimages of the road as the vehicle is traveling for collision avoidancepurposes, provide structure recognition, such as roadway signs, etc.Other vehicle vision applications include vehicle lane sensing systemsto sense the vehicle travel lane and drive the vehicle in thelane-center. Many of these known lane sensing systems detectlane-markers on the road for various applications, such as lanedeparture warning (LDW), lane keeping (LK), lane centering (LC), etc.,and have typically employed a single camera, either at the front or rearof the vehicle, to provide the images that are used to detect thelane-markers.

It is also known in the art to provide a surround-view camera system ona vehicle that includes a front camera, a rear camera and left and rightside cameras, where the camera system generates a top-down view of thevehicle and surrounding areas using the images from the cameras, andwhere the images overlap each other at the corners of the vehicle. Thetop-down view can be displayed for the vehicle driver to see what issurrounding the vehicle for back-up, parking, etc. Future vehicles maynot employ rearview mirrors, but may instead include digital imagesprovided by the surround view cameras.

Various vehicle systems of the type being discussed herein require thatthe position and orientation of the vehicle be known. Currently, modernvehicles typically rely on a global navigation satellite system (GNSS),such as GPS, that provides signals to a vehicle display to identifyvehicle location.

Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I)communications systems, sometimes referred generally as V2X systems, areknown to those skilled in the art, and require a minimum of one entityto send information to another entity. For example, manyvehicle-to-vehicle safety applications can be executed on one vehicle bysimply receiving broadcast messages from a neighboring vehicle. Thesemessages are not directed to any specific vehicle, but are meant to beshared with a vehicle population to support the particular application.In these types of applications where collision avoidance is desirable,as two or more vehicles talk to each other and a collision becomesprobable, the vehicle systems can warn the vehicle drivers, or possiblytake evasive action for the driver, such as applying the brakes.Likewise, traffic control units can observe the broadcast of informationand generate statistics on traffic flow through a given intersection orroadway.

When roadways cross intersections are created. In order to preventvehicles from colliding with each other at an intersection, some type oftraffic control mechanism, such as stop signs, yield signs, trafficlights, etc., are generally provided so that perpendicularly orcross-traveling traffic can travel safely through the intersection.However, intersections, especially high traffic intersections, are stillthe cause of many vehicle collisions and traffic accidents.

Known object detection sensor systems that attempt to warn the driver ofpotential collisions while making a left or right turn at anintersection typically rely on a single algorithm for providing thewarning regardless of where the host vehicle is relative to theintersection and at what speed and direction the host vehicle istraveling. Typically these types of algorithms are ineffective becausethey are unable to consistently warn the driver in time before thecollision occurs. More particularly, different vehicles are operated atdifferent speeds and aggressiveness, where some vehicles approach anintersection very quickly while others approach the intersection moreslowly, and where the host vehicle may stop in the intersection to allowopposing traffic to pass before making the turn. Because of thesevariations such algorithms are ineffective in providing a warning in asuitable amount of time, and thus, improvements need to be made beforethey can be provided on commercial vehicles.

SUMMARY OF THE INVENTION

The present disclosure describes a system and method for warning avehicle driver of a potential collision when turning left or right at ornear an intersection, where the system and method provide additionalanalysis to limit false positive and false negative warnings based onspecialized circumstances. The method includes determining if the hostvehicle is likely to turn at or near the intersection with apredetermined level of confidence, and obtaining speed, velocity andposition data of the host vehicle and any relevant remote vehicles. Themethod determines a predicted path of the host vehicle and the remotevehicles based on the speed, velocity and position data, and issues awarning to a driver of the host vehicle if the host vehicle and one ofthe remote vehicles may collide based on the speeds, acceleration andpredicted paths. If the host vehicle is in a specialized circumstance,the method provides additional collision analysis to reduce falsepositive warnings and/or false negative warnings.

Additional features of the present invention will become apparent fromthe following description and appended claims, taken in conjunction withthe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a vehicle including various components foridentifying operation of the vehicle and detecting objects around thevehicle;

FIG. 2 is an illustration of an intersection showing a host vehiclemaking a left turn;

FIG. 3 is an illustration of an intersection showing a host vehiclemaking a right turn;

FIG. 4 is an illustration of an intersection showing the position of ahost vehicle making a right turn and an opposite direction remotevehicle making a left turn at different periods in time;

FIG. 5 is an illustration of an intersection showing the position of ahost vehicle making a right turn and a lateral remote vehicle travelingstraight through the intersection at different periods in time;

FIG. 6 is an illustration of an intersection showing the position of ahost vehicle making a left turn and a lateral remote vehicle travelingstraight through the intersection at different periods in time;

FIG. 7 is an illustration of an intersection showing the position of ahost vehicle making a left turn and an opposite direction remote vehicletraveling straight through the intersection at different periods intime;

FIG. 8 is a time line showing the times illustrated in FIGS. 4-7;

FIG. 9 is an illustration of an intersection showing a host vehiclewaiting in the intersection and then making a left turn and an oppositedirection remote vehicle traveling straight through the intersection atdifferent periods in time;

FIG. 10 is a time line showing the times illustrated in FIG. 9;

FIG. 11 is a flow chart diagram showing a process for assessing threatswhen a vehicle is making a left or right turn at an intersection;

FIG. 12 is a flow chart diagram showing a process for deciding if it issafe for a vehicle to enter an intersection;

FIG. 13 is a flow chart diagram showing the process of FIG. 12 andemploying V2I data;

FIG. 14 is a flow chart diagram showing a process for determiningwhether it is safe to proceed through waiting-regions in anintersection;

FIG. 15 is a flow chart diagram showing a process for determiningwhether to allow a vehicle to enter a no-waiting region in anintersection;

FIG. 16 is an illustration of an intersection showing a host vehicletraveling in an outside travel lane of a dual-lane roadway and making alane change;

FIG. 17 is an illustration of an intersection showing a host vehicletraveling in an inside lane of a dual-lane roadway and making a leftturn;

FIG. 18 is an illustration of a host vehicle traveling in outside laneof a dual-lane roadway making a lane change;

FIG. 19 is an illustration of a host vehicle traveling in an inside laneof a dual-lane roadway and turning into a driveway;

FIG. 20 is an illustration of a host vehicle traveling along a roadwayand turning into a driveway just before an intersection; and

FIG. 21 is an illustration of a vehicle making a left turn at anintersection.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The following discussion of the embodiments of the invention directed toa system and method for assessing a collision threat when a host vehicleis turning that includes providing additional analysis to limit falsepositive and false negative warnings based on specialized circumstancesis merely exemplary in nature, and is in no way intended to limit theinvention or its applications or uses.

As will be discussed in detail below, the present invention proposescollision assessment algorithms that provide information to a driver ofa host vehicle, a warning to the driver, and/or automatic braking and/orsteering control, if necessary, when a vehicle is turning right or leftat an intersection for a confidence level of driver turning intent basedon a determined collision threat with other vehicles traveling in andthrough the intersection. The determination of whether to give theinformation, warning and/or automatic braking includes separating thearea in which the vehicle may travel in the intersection when it turnsinto separate regions, namely, an approach region before theintersection, a waiting region inside the intersection and a no-waitingregion inside the intersection, which are different areas in theintersection depending on whether the vehicle is turning right orturning left, where a separate algorithm for collision threat assessmentis employed for each region to identify the collision threats. Thealgorithms identify the various remote vehicles that may be entering theintersection, that are in the intersection, that are turning at theintersection, etc., analyze the speed and location of those vehiclesrelative to the speed and location of the host vehicle, and identify thecollision threat potential for the approach region, the waiting regionand the no-waiting region. The algorithms can use any information thatis available to identify necessary parameters, such as a map database,V2X communications, object detection sensors on the vehicle, cameras onthe vehicle, etc., where the specific location of the intersection andthe host vehicle may not need to be overly accurate.

It is noted that the discussion herein is specific to vehicle traveldirection on the right, where a vehicle making a left turn will crosslanes for oncoming traffic. However, it is stressed that the algorithmsand discussion herein equally apply to those countries and roadwayswhere vehicles travel on the left and would cross in front of oncomingtraffic when making a right turn. It is also noted that, as will beunderstood by those skilled in the art, the algorithm parametersdescribed here can be adjusted to suit different driver-selectableconfigurations, such as aggressive, normal, conservative, etc., tomodify the warning/output timing provided by the particular feature.Alternatively, the system can itself adjust these parameters based onthe driving style of the driver.

FIG. 1 is a simple illustration of a vehicle system 10 that includes avehicle 12 having a map database 14, a navigation system 16, anoperation controller 18, a warning device 20, sensors/detectors 32 and avehicle controller 22. The controller 18 is intended to represent all ofthe separate modules, controllers, processors, electronic control units,etc. that are necessary to perform and run the various algorithms andprocesses discussed herein. The map database 14 stores map informationat any level of detail that is available, including specific informationabout intersections, such as the number of lanes, the lane travelpatterns, etc. The map database 14 operates in association with thenavigation system 16 to display the various maps and other informationthat is available, and allow a user to input, plan and display a route.The sensors/detectors 32 are intended to represent any and all objectdetection sensors or cameras on the vehicle 12, such as forward, rearand side cameras, back-up cameras, lidar sensors, long range radardetectors, short range radar detectors, etc., located at any position onthe vehicle 12. The warning device 20 can be any suitable warningdevice, such as display icons, haptic seat, chimes, etc. The controller22 controls the operation of the vehicle 12, including steering, brake,throttle, etc., possibly for both autonomous and semi-autonomouscapabilities, and provides any other vehicle control consistent with thediscussion herein. The vehicle 12 also includes a wireless port 24 thatallows the vehicle 12 to wirelessly transmit messages and receivemessages from many sources, such as the Internet 26, a satellite 28, awireless infrastructure 30, etc. The wireless port 24 also allows thevehicle 12 to provide V2I and V2V communications, if available.

FIG. 2 is an illustration 40 of a roadway intersection 42, whereroadways 44 and 46 cross defining an intersection region 48. The roadway44 includes two travel lanes 50 and 52 for traffic flow in one directionand two travel lanes 54 and 56 for traffic flow in an oppositedirection. Likewise, the roadway 46 includes two travel lanes 58 and 60for traffic flow in one direction and two travel lanes 62 and 64 fortraffic flow in an opposite direction. Stop bar 68 is a stop linelocation for the lanes 50 and 52 at the intersection 42, stop bar 70 isa stop line location for the lanes 54 and 56 at the intersection 42,stop bar 72 is a stop line location for the lanes 58 and 60 at theintersection 42, and stop bar 74 is a stop line location for the lanes62 and 64 at the intersection 42. The travel lanes 50, 52, 54, 56, 58,60, 62 and 64 can be any of straight through only, left turn only, rightturn only or shared straight through, left turn and/or right turn.

The intersection 42 shows a host vehicle 80 traveling in the travel lane56 that is approaching the intersection 42, and making a left turn intoone of the travel lanes 58 or 60 along a predicted path 88, wherefurther discussion of how the predicted path 88 is determined isprovided below. An opposite direction remote vehicle 84 is showntraveling in the travel lane 52 and a lateral remote vehicle 86 is showntraveling in the travel lane 62. As will be discussed in detail below,the area inside and around the intersection region 48 is segmented intodifferent regions based on the predicted path 88 and location of thehost vehicle 80, the predicted path and locations of the remote vehicles84 and 88, intersection geometry including number of travel lanes, etc.For example, the algorithms may identify an approach region, a waitingregion and a no-waiting region in the intersection 42, and may furthersegment those regions into smaller regions depending on whether the hostvehicle 80 is intending to make a right or left turn, the size of theintersection 42, the location and speed of other vehicles and objects inthe intersection 42, etc.

In the illustration 40, the host vehicle 80 is shown in an approachregion 90 before the intersection region 48. When the host vehicle 80makes the left turn, it will enter the intersection region 48, and thenenter a waiting region 92, where the vehicle 80 may wait to make theleft turn when opposing traffic is present. The determination of whetherthe vehicle 80 is going to stop in the waiting region 92 is based on,for example, the road wheel angle and/or the yaw rate of the vehicle 80and/or whether the driver is applying the brakes of the host vehicle 80,where the vehicle 80 is decelerating. A no-waiting region 94 is alsodefined in the intersection region 48 and is in the pathway of oncomingtraffic, such as the remote vehicle 84, where the host vehicle 80 is notallowed to stop. Since the vehicle 80 may be waiting in the waitingregion 92 for oncoming traffic to clear, but may be creeping along inthe waiting region 92 as it moves closer to the no-waiting region 94,the waiting region 92 is separated into zones 96 as the vehicle 80 mayslightly move along in the waiting region 92 to predict the collisionthreat more accurately.

In order to determine when the host vehicle 80 is entering the regions92 and 94, it is necessary to predict the path 88 of the host vehicle 80while it is making the turn. Many algorithms exist in the art forpredicting the path of a vehicle, and typically use the vehicle yaw rateand/or road wheel angle to define that path, which are generally notvery accurate because the yaw rate may not be as sharp as the requiredturning radius to make the turn. Thus, when the host vehicle 80 ismaking a turn at the intersection 42, the turning radius of the hostvehicle 80 may not exactly match the angle necessary to enter the lane58 or 60, and thus the present invention determines the predicted path88 of the vehicle 80 based not only on vehicle position and dynamics,but also on the geometry of the intersection 42. In one embodiment, thealgorithm obtains the predicted path 88 of the host vehicle 80 usingvehicle yaw rate and/or road wheel angle, and then refines thatpredicted path based on the geometry of the intersection 42 depending onwhether that information is available, and specifically the locations ofthe lanes 58 and 60.

FIG. 3 is an illustration 100 of the intersection 42 similar to theillustration 40, where like elements are identified by the samereference number, showing the host vehicle 80 in the approach region 90before the intersection 42 and intending to make a right turn into thelane 62 or 64 along predicted path 102. As with the left turningscenario discussed above, a waiting region 104 where the host vehicle 80can stop and wait in the intersection region 48 and a no-waiting region106 where the host vehicle 80 cannot stop in the intersection region 48because of collision threats coming from the lateral direction by, forexample, the lateral remote vehicle 86 are identified.

Based on the illustrations 40 and 100 shown in FIGS. 2 and 3, thepresent invention identifies when to provide possible collision threatinformation to the driver, provide a warning to the driver of a possiblecollision, and/or provide automatic braking in response to a detectedcollision threat. As mentioned above, the intersection 42 is separatedinto various regions and zones depending on intersection geometry andvehicle speed and location, and different algorithms may be employed toidentify the collision threats based on this available information.

FIGS. 4, 5, 6 and 7 show four exemplary situations of possible collisionthreats from the opposite direction remote vehicle 84 and the lateraldirection remote vehicle 86, where box 118 represents a portion of theintersection region 48 and is referred to herein as a collisionassessment region. Particularly, FIG. 4 is an illustration 110 showing acollision threat from the opposite direction remote vehicle 84 when thehost vehicle 80 is turning right along path 98 and the remote vehicle 84is turning left along path 138 in the assessment region 118; FIG. 5 isan illustration 112 showing a collision threat from the lateral remotevehicle 86 when the host vehicle 80 is turning right along the path 98and the remote vehicle 86 is traveling straight along path 140 thoughthe assessment region 118; FIG. 6 is an illustration 114 showing acollision threat from the lateral remote vehicle 86 when the hostvehicle 80 is turning left along path 142 and the lateral remote vehicle86 is travelling straight along the path 140 through the assessmentregion 118; and FIG. 7 is an illustration 116 showing a collision threatfrom the opposite direction remote vehicle 84 when the host vehicle 80is turning left along the path 142 and the remote vehicle 84 istravelling straight along path 144 through the assessment region 118. Itis noted that for the illustration 116 in FIG. 7, in this scenario, thehost vehicle 80 does not stop and wait in the intersection region 48 foroncoming traffic to clear.

The collision assessment algorithms assess the threats of a collision inthe assessment region 118 by analyzing the expected position andpredicted path of the host vehicle 80, the expected position andpredicted path of the opposite direction remote vehicle 84, and theexpected position and predicted path of the lateral direction remotevehicle 86, which are determined by sensor information and dynamics ofthe velocity, acceleration and predicted path of the host vehicle 80 andthe remote vehicles 84 and 86. In the illustrations 110, 112, 114 and116, given the current location, predicted path and speed of the hostvehicle 80, and the current location, predicted path and speed of theremote vehicles 84 and/or 86, a collision zone 146 is defined that isthe area where the host vehicle 80 could collide with the remote vehicle84 or 86 when the host vehicle 80 is turning right in the assessmentregion 118, a collision zone 148 is defined that is the area where theother direction remote vehicle 84 could collide with the host vehicle 80when the remote vehicle 84 is turning left in the assessment region 118,a collision zone 158 is defined that is the area where the lateraldirection remote vehicle 86 could collide with the host vehicle 80 whenthe remote vehicle 86 is traveling straight through the assessmentregion 118, a collision zone 166 is defined that is the area where thehost vehicle 80 could collide with the remote vehicle 84 or 86 when thehost vehicle 80 is turning left in the assessment region 118, and acollision zone 168 is defined that is the area where the other directionremote vehicle 84 could collide with the host vehicle 80 when the remotevehicle 84 is traveling straight through the assessment region 118 andthe host vehicle 80 is turning left in the assessment region 118.

In each of the illustrations 110, 112, 114 and 116, box 120 representsthe position of the host vehicle 80 at time T=T₀, where T₀ is thecurrent time, box 122 represents the position of the host vehicle 80when it is about to enter the collision zone 146 or 166 at time T=T₁,box 124 represents the position of the host vehicle 80 just after it hasleft the collision zone 146 or 166 at time T=T₂, box 126 represents theposition of the opposite direction remote vehicle 84 at time T=T₀, box128 represents the position of the lateral direction remote vehicle 86at time T=T₀, box 130 represents the position of the opposite directionremote vehicle 84 when it is about to enter the collision zone 148 or168 at time T=T₃, box 132 represents the position of the lateraldirection remote vehicle 86 when it is about to enter the collision zone158 at time T=T₃, box 134 represents the position of the oppositedirection remote vehicle 84 just after it has left the collision zone148 or 168 at time T=T₄, and box 136 represents the position of thelateral direction remote vehicle 86 when it has just left the collisionzone 158 at time T=T₄. It is noted that as shown by the illustrations110, 112, 114 and 116, the collision threat assessment algorithms assessthe collision risk from the lateral remote vehicle 86 before the hostvehicle 80 enters the assessment region 118 regardless of whether thehost vehicle 80 is turning right or left, assess the collision risk fromthe opposite direction remote vehicle 86 before the host vehicle entersthe assessment region 118 if the host vehicle is turning right, andassess the collision risk from the opposite direction remote vehicle 86after the host vehicle 80 enters the assessment region 118 when the hostvehicle 80 is turning left.

FIG. 8 is a time line 150 that applies to all of the illustrations 110,112, 114 and 116 in FIGS. 4-7 showing each of the times T₀, T₁, T₂, T₃and T₄. When analyzing the threat assessments for the above describedand other scenarios, the collision assessment algorithms calculate theduration that the host vehicle 80 is expected to occupy the collisionzone 146 or 166 from time T₁ to time T₂, represented by line 152 in thetime line 150. The algorithm also calculates the duration that theopposite direction remote vehicle 84 and/or the lateral remote vehicle86 is expected to occupy the collision zones 158 or 166 from time T₃ totime T₄, represented by line 154 in the time line 150. If there is anoverlap in the times represented by the lines 152 and 154, then thealgorithm also identifies a time remaining T_(REM) that is the time itwill take the host vehicle 80 to enter the collision zone 146 or 166represented by line 156 in the time line 150. The algorithm will providecollision threat information, issue a warning, and/or provide automaticbraking when T_(REM)<T_(THR), where the time threshold T_(THR) for awarning is determined experimentally considering driver reaction timeand system delays, and the time threshold T_(THR) for automatic brakingis determined by considering system delays. In one non-limiting example,if T_(REM) is greater than ten seconds, then no action is performed, ifT_(REM) is between four and ten seconds, then potential collisioninformation is provided to the vehicle driver, such as an icon beingprovided on the display, if T_(REM) falls below four seconds, then thealgorithm provides a warning, such as a chime or haptic seat, and ifT_(REM) indicates an immediate collision threat, then the algorithm mayprovide automatic braking and/or steering.

The discussion above for the illustration 116 when the host vehicle 80is turning left will likely not provide the desirable results if thehost vehicle 80 stops in the waiting region 92 to wait for oppositedirection traffic to clear because of delays in receiving updatedvelocity and acceleration values for the host vehicle 80, and time tocollision (TTC) values based algorithms may not be sufficient to providewarnings to the driver before entering the no-waiting region 94. Inother words, the time T_(REM) is too short to provide accurate results.

FIG. 9 is an illustration 160 showing this scenario and FIG. 10 is atimeline 162 for the illustration 160, where like elements to theillustration 116 and the time line 150 are shown by the same referencenumbers. In the illustration 160, the host vehicle 80 is at time T=T₀when it is in the waiting region 92 and is stopped or creeping alongwaiting for the traffic to clear. In this scenario, the time T₁ when thehost vehicle 80 may enter the collision zone 166 may be small as shownby time line 164, which represents time T_(REM), and the algorithm willnot allow a warning to be given because of uncertainty. The predictedpath 142 of the host vehicle 80 is based on the vehicle's current yawrate and/or road wheel angle, but is probably not a likely path 108 ofthe host vehicle 80 if it is making the left turn because the vehicle 80will be angled straighter relative to its travel lane to better allowthe driver to watch for oncoming traffic. Thus, the algorithm will notinform the driver or provide a warning based on these parameters.

In this embodiment, the algorithm instead detects the start of the leftturn of the host vehicle 80 by the release of the vehicle brakes,considers the closeness between the predicted path 142 based on roadwheel angle and the likely path 108 of the host vehicle 80 during theleft turn, and the time T₃-T₀ that identifies when the remote vehicle 84will enter the collision zone 168. If the host vehicle 80 is in thewaiting region 92 and is creeping, when the yaw angle changes and/or theroad wheel changes indicating a turn, the algorithm can determine thatthe host vehicle 80 is about to start the turn.

The correlation (closeness) between the predicted path 142 and thelikely path 108 of the host vehicle 80 can be computed by a correlationcoefficient C_(PL), which is defined as:

$\begin{matrix}{{C_{PL} = {C_{X}*C_{Y}}},{{where}\text{:}}} & (1) \\{{C_{X} = \frac{{N*\Sigma \; X_{Pi}*X_{Li}} - {\Sigma \; X_{Pi}*\Sigma \; X_{Li}}}{\sqrt{{N*{\Sigma \left( X_{Pi} \right)}^{2}} - {\left( {\Sigma \; X_{Pi}} \right)^{2}*}}\sqrt{{N*\Sigma \; \left( X_{Li} \right)^{2}} - \left( {\Sigma \; X_{Li}} \right)^{2}}}},} & (2) \\{{C_{Y} = \frac{{N*\Sigma \; Y_{Pi}*Y_{Li}} - {\Sigma \; Y_{Pi}*\Sigma \; Y}}{\sqrt{{N*{\Sigma \left( Y_{Pi} \right)}^{2}} - {\left( {\Sigma \; Y_{Pi}} \right)^{2}*}}\sqrt{{N*\Sigma \; \left( Y_{Li} \right)^{2}} - \left( {\Sigma \; Y_{Li}} \right)^{2}}}},} & (3)\end{matrix}$

and where C_(X) and C_(y) represent the correlation between thepredicted path 142 and the likely path 108 in the X and Y directions,and are calculated using the closeness between individual nodes N, where(X_(Pi), Y_(Pi)) and (X_(Li), Y_(Li)) correspond to (X,Y) coordinates ofthe nodes N on the predicted path 142 and the likely path 108,respectively.

The algorithm issues a warning and/or provides automatic braking if thehost vehicle brakes are released, C_(PL)>C_(THR), where C_(THR) is acoefficient threshold that is determined experimentally, andT₃-T₀<T_(THR2), where T_(THR2) is determined experimentally consideringdriver reaction time and system delays for issuing a warning, such assystem delays for providing automatic braking. Using the correlationcoefficient C_(PL) is one way in which a determination of a warningshould be issued, where determining road wheel angle can be another way.It is also noted that this analysis for a vehicle waiting in the waitingregion 92 for traffic to clear equally applies to a vehicle waiting inthe waiting region 104 for traffic to clear to make a right turn.

FIG. 11 is a flow chart diagram 170 showing a process for assessing acollision threat when the host vehicle 80 is making a left turn or aright turn at the intersection 42. At box 172, the collision assessmentalgorithm determines whether the host vehicle 80 is arriving at theintersection 42 and the confidence level of whether the host vehicle 80is about to make a left or right turn based on all of the informationthat is available to the algorithm as discussed herein, such as dataavailable from onboard sensors on the vehicle 80, wireless data receivedfrom infrastructure, such as V2X, Onstar™, Internet Cloud, the travellane that the vehicle 80 is in, the speed of the vehicle 80, turn signalactivation, whether the vehicle 80 is slowing down, etc., all providedat box 174.

Once the algorithm makes the determination of a turn at a certainconfidence level, the algorithm then determines where the host vehicle80 will be located, and in particular the location of the host vehicle80 in the intersection 42, based on the predicted path 88 or 102 of thevehicle 80 using the available information including intersectiongeometry at box 176. The algorithm then separates and segments theintersection 42 into the various regions, such as the waiting region 96or 104 and the no-waiting region 94 or 106 based on the intersectiongeometry and vehicle state at box 178. The algorithm then assesses thecollision threat level for the particular region in the intersection 42that the host vehicle 80 is about to enter depending on which region itis in at box 180. The algorithm then determines whether entering theparticular region based on all the information available to it is safebased on collision threats to the vehicle 80 at decision diamond 182. Ifentering the particular region is not safe at the decision diamond 182,then the algorithm will cause certain actions to be performed at box184, such as providing a warning, automatic braking, etc., as discussedabove, and then will return to the box 180 for assessing the collisionthreats. If the algorithm determines that it is safe for the hostvehicle 80 to enter the particular region at the decision diamond 182,then algorithm allows the vehicle 80 to enter the region at box 186, anddetermines if the left turn across path or right turn (LTAP/RT) has beencompleted at decision diamond 188, and if not, returns to the box 180 toassess the collision threats. Otherwise, the algorithm ends at box 190.

FIG. 12 is a flow chart diagram 194 showing a process for determiningwhether the host vehicle 80 can enter the intersection 42, where thecollision assessment algorithm starts at box 196. It is noted that thealgorithm associated with the flow diagram 194 is repeatedly performeduntil the host vehicle 80 enters the intersection 42. At decisiondiamond 198 the algorithm determines whether the vehicle 80 is allowedto enter the intersection 42, by, for example, determining whether thereis a stop sign, determining whether a signal light is red, etc., usingthe available resources, such as camera data provided by cameras on thevehicle 80, lidar sensors, V2X communications from infrastructureincluding the signal light, etc. If the host vehicle 80 is allowed toenter the intersection 42 at the decision diamond 198, then thealgorithm assesses the immediate collisions threats from other vehiclesin the intersection 42 at box 200, such as threats coming fromcross-traffic on the left side. It is noted that assessing the immediatecollision threats from other vehicles from the left side is for thoseroadway and countries where vehicles travel on the right. If the systemis being employed on vehicles in countries where travel is on the left,then the immediate threats will likely come from the right side. Thealgorithm then determines whether it is safe for the vehicle 80 toproceed into the intersection 42 at decision diamond 202, and if so, thevehicle 80 enters the intersection 42 and proceeds through the waitingregion at box 204, where the algorithm ends at box 206.

If the vehicle 80 is not allowed to enter the intersection 42 at thedecision diamond 198 or it is not safe to enter the intersection 42 atthe decision diamond 202, then the algorithm determines whether the timethat the vehicle 80 will take at the current driving conditions to astop bar before the intersection 42 is less than the time it takes forthe vehicle 80 to stop before the stop bar at decision diamond 208. Inother words, the algorithm determines based on the driving behavior ofthe driver, the speed of the vehicle 80, the road surface conditions,etc., whether the driver is likely to stop the vehicle 80 at the stopbar before the intersection 42, and, if the time to the stop bar is lessthan the time to stop, the algorithm provides automatic braking to occurat box 210 and the algorithm ends at the box 206. If the algorithmdetermines that the vehicle driver is likely to stop before the stop barat the decision diamond 208, then the algorithm determines whether thetime to stop at the stop bar under the current vehicle drivingconditions is less than the time that the vehicle 80 will take at thecurrent driving conditions to stop at the stop bar plus some additionalpredetermined delta time at decision diamond 212 to determine whetherthe vehicle 80 is approaching the intersection to quickly, but thedriver still will be able to stop the vehicle 80 at the stop bar. If thetime to the stop bar is less than the time that it takes to stop plusthe delta time at the decision diamond 212, meaning that the driver isapproaching the intersection 42 to quickly, the algorithm will issue awarning a box 214, and the algorithm ends at the box 206. If the time tothe stop bar is not less than the time to the stop bar plus the deltatime at the decision diamond 212, then the algorithm takes no action andthe algorithm ends at the box 206.

The algorithm shown by the flow chart diagram 194 can be improved ifthere are V2I capabilities on the host vehicle 80. FIG. 13 is a flowchart diagram 220 showing such improvements, where like elements to theflow chart diagram 194 are identified by the same reference number. Inthis process, the algorithm determines whether signal space and timing(SPAT) data is available at decision diamond 222, and if not, proceedsto the decision diamond 198 to follow the same process as in the flowdiagram 194. SPAT data wirelessly provides timing information that canbe received by the host vehicle 80 that gives values as to how long thesignal light will stay in its current position, such as in the greenposition. If the SPAT data is available at the decision diamond 222, thealgorithm determines whether the time to a red light is greater than ittakes for the vehicle 80 to cross the intersection 42 at decisiondiamond 224, and if it is, proceeds to the box 200 to assess collisionthreats. Because the V2I data is available and the timing of the signallights is available to the algorithm, this information can be used tofurther assess potential collision threats by determining whether thevehicle 80 will still be in the intersection 42 when the signal lightturns red. If the time to a red light is not greater than the time tothe cross the intersection 42 at the decision diamond 224, then thealgorithm proceeds to the decision diamond 208 to determine whether thecurrent vehicle driving operation will allow the vehicle driver to stopat the stop bar.

FIG. 14 is a flow chart diagram 230 showing a process for determiningdecisions to proceed from the waiting region 92 or 104 to the no-waitingregion 94 or 106 inside of the intersection 42, where the collisionthreat assessment is determined based on the specific location of thevehicle 80 while the host vehicle 80 is in the waiting region 92 or 104.In this scenario, for earlier zones in the waiting region 92 or 104, thethreats may come from cross-traffic from the left, and for later zonesin the waiting region 92 or 104, the threats may come from cross-trafficfrom the right.

The algorithm starts at box 232 and determines if the next region thatthe vehicle 80 will enter is the no-waiting region 94 or 106 at decisiondiamond 234. If the next region is the no-waiting region 94 or 106, thenthe algorithm performs conditioning at box 236 to enter the no-waitingregion 94 or 106 and the algorithm ends at box 238. If the next regionis not the no-waiting region 94 or 106 at the decision diamond 234, thealgorithm assesses the relevant collision threats at box 240 asdiscussed herein and determines whether any threats are present atdecision diamond 242. If there are no collision threats present at thedecision diamond 242, the algorithm allows the vehicle 80 to proceed tothe no-waiting region 94 or 106 at box 244 and the algorithm ends at thebox 238. If there are collision threats detected at the decision diamond242, the algorithm determines based on current vehicle and intersectionconditions whether the time to the end of the region that the vehicle 80is in is less than a time to stop to avoid the threat at decisiondiamond 246. If the time to the end of that region is less than the timeto stop at the decision diamond 246, then the algorithm automaticallyapplies the brakes and stops the vehicle 80 before the stop bar at box248 and the algorithm each at the box 238. If the time to the end ofthat region is not less than the time to stop at the decision diamond246, then the algorithm determines whether the time to the end of theregion is less than the time to the stop bar plus some predetermineddelta time at decision diamond 250, and if so, the algorithm issues awarning at the box 252 and the algorithm ends at the box 238.

FIG. 15 is a flow chart diagram 256 of a portion of the collisionassessment algorithm discussed herein that includes making decisionswhether to enter the no-waiting region 94 or 106 inside the intersection42. In this process, the collision threat assessment is conducted forthreats from opposite vehicle traffic and pedestrians to decide whethera driver warning or automatic braking control should be performed. Thealgorithm starts at box 258 and assesses collision threats from trafficcoming in the opposite direction from the host vehicle 80 at box 260.Once those threats are assessed, the algorithm determines whether it issafe for the vehicle 80 to enter the no-waiting region 94 or 106 atdecision diamond 262, and if so, assesses any threats from trafficcoming from the right travel lanes at box 264. The algorithm thendetermines based on that assessment whether the host vehicle 80 cansafely enter the no-waiting region 94 or 106 at decision diamond 266,and if so, the algorithm determines whether pedestrians are present inthe cross-walk at box 268. Based on the assessment of pedestrians, thealgorithm determines whether it is safe to enter the intersection 42 atdecision diamond 270, and if so, continues through the no-waiting region94 or 106 and exits the intersection 42 at box 272, and the algorithmends box 274. If the algorithm determines that it is not safe for thevehicle 80 to proceed at any of the decision diamonds 262, 266 and 270,then the algorithm issues a driver warning and/or automatically appliesthe brakes based on the discussion herein at box 276.

The collision threat assessment for a host vehicle turning left or rightat an intersection as discussed above could be the subject of a numberof situations that would cause the collision assessment algorithm toprovide a warning or automatic braking to occur when no threat ispresent and not cause a warning to be issued or brakes applied if athreat is present, referred to herein as positive false alarms andnegative false alarms, respectively. Positive false alarms and negativefalse alarms could occur when accurate data, such as digital maps and/ordriver data, such as turn signals, brake position, etc, are notavailable. The following discussion provides six challenging driving usecase scenarios where positive or negative false alarms are more likelyto occur and some solutions to those challenges that could improve thecollision threat assessment discussion above. The reduction of positiveand negative false alarms can be performed as discussed herein usingavailable visual data from cameras, radar sensors, lidar sensors, V2Xinputs, path history, map data, driver inputs, such as turn signalstatus, head pose, eyes, etc.

The first use case scenario is a false positive and includes making alane change near an intersection versus turning left at theintersection, and is illustrated in FIGS. 16 and 17, where a driver mayuse turn signals to indicate both driving maneuvers. FIG. 16 is anillustration 280 and FIG. 17 is an illustration 302 of a roadway 282having an outer travel lane 284 and an inner travel lane 286 and aroadway 288 for opposite direction traffic having an outer travel lane290 and an inner travel lane 292, where the roadways 282 and 288 areseparated by a center line 294. A crossing roadway 296 crosses theroadways 282 and 288 creating an intersection 298. A host vehicle 300 isshown traveling along the outer travel lane 284 in FIG. 16 and is makinga lane change maneuver to the travel lane 286 along path 304. In FIG.17, the host vehicle 300 is traveling in the inner travel lane 286 andis making a left turn along path 306 into the crossing roadway 296 infront of other direction vehicles 302 traveling in the travel lanes 290and 292, as shown.

For both of these maneuvers, the trajectory of the host vehicle 300during the initial movement to make the lane change or the left turn aresimilar, thus making it difficult to distinguish the maneuver especiallywhen lane-level maps and/or high accuracy GPS data is not available.Typically, commercial navigation maps do not include lane level mapsshowing the number of lanes in a particular roadway. According to theinvention, the collision assessment algorithms discussed herein will usewhatever data is available to determine whether the host vehicle 300 istraveling in the travel lane 284 or the travel lane 286, where once thatdetermination is made, then the algorithm will know better whether thehost vehicle 300 is changing lanes from the travel lane 284 to thetravel lane 286 or turning from the travel lane 286 to the crossingroadway 296. If the navigation maps include the number of lanes, thenthat information can be used to determine what lane the host vehicle 300is in, and whether it is making a lane change or left turn. If thenavigation maps do not include the separate travel lanes, then thealgorithm can use other available data, such as the position and traveldirection of other vehicles 304 in both the opposite direction or thesame direction and location of objects, such as curbs, to determine whatlane the host vehicle 300 is in. For example, if there are no samedirection vehicles on the left of the host vehicle and there are otherdirection vehicles on the immediate left of the host vehicle, then thehost vehicle 80 is in the left-most travel lane, here the travel lane286.

In another situation that could provide false positives, a vehicletraveling in a dual-lane roadway may be changing lanes or may be turninginto a driveway with or without a left turn lane. This is a difficultscenario to distinguish because most digital maps do not have drivewaysin the database. Further, some roadways have left turn lanes, helpingprovide an indication that the vehicle is making a left turn if itenters the left turn lane.

This situation is illustrated in FIGS. 18 and 19, where FIG. 18 is anillustration 308 and FIG. 19 is an illustration 330 showing a dual-laneroadway 310 for travel in one direction including an outside travel lane312 and an inside travel lane 314 and a dual-lane roadway 316 for travelin an opposite direction including an outside travel lane 318 and aninside travel lane 320, where the roadways 310 and 316 are separated bycenter line 322. In FIG. 18, a host vehicle 324 is traveling in theoutside travel lane 312 and is making a lane change into the insidetravel lane 314 along path 332. In FIG. 19, the host vehicle 324 istraveling in the inside travel lane 314 and is making a left turn alongpath 334 into a driveway 326 in front of opposing vehicles 328 travelingin the roadway 316, as shown.

In order to reduce the false positives for this situation, the algorithmcan use images from a forward vision camera to identify the color andtype of lane marking, such as dash lines, solid white lines, solidyellow lines etc., the position of external objects, vehicles travelingin the same direction or a different direction, etc., to identify whatmaneuver the host vehicle 324 is performing. For example, left turnlanes are often designated by solid yellow lines, which when identifiedby the camera, can provide an indication to the algorithm that the hostvehicle 324 is turning into a left turn lane to make a left turn.

In another situation that sometimes could provide a false positive for avehicle turning at an intersection is a situation where the host vehicleis turning into a driveway located near the intersection. As discussedabove, the predicted path of the host vehicle when turning at anintersection may be corrected based on the intersection geometry whenthe predicted path indicates that the host vehicle may travel outside ofthe intersection. If the algorithm makes a correction to a predictedpath of the host vehicle when the actual path of the host vehicle isturning into the driveway instead of at the intersection, then thealgorithm could issue false warnings based on its interpretation thatthe host vehicle is actually turning at the intersection.

FIG. 20 depicts this situation and is an illustration 340 showingperpendicular roadways 342 and 344 defining an intersection 346, wherethe roadway 344 includes opposing traffic lanes 348 and 350 separated bycenter line 352. A host vehicle 354 is traveling in the lane 348 wherethe collision assessment algorithm determines that it is intending tomake a left turn, either by turn signal activation or otherwise. In somesituations, the algorithm may determine that the host vehicle 354 isgoing to turn left into the roadway 344 as indicated by predicted path358 when in fact the vehicle 354 is turning into a driveway 356, such asat a gas station, as indicated by actual path 360. Thus, the positionand speed of different opposite direction vehicles 362 determine thelevel of threat for the host vehicle 354.

In one known collision assessment algorithm design, the left turningfeatures of the algorithm are activated within some detected parameterdistance D, such as 100 meters, before the intersection 346 based on amap database and turn signal activity, for example, and that is why thealgorithm may predict the path of the vehicle 354 to be on the path 358before the intersection 346. In order to reduce or prevent this falsenegative, the present invention proposes causing the algorithm to obtainthe size of the intersection 346, such as the number of lanes in alldirections through the intersection 346, from navigation maps, orotherwise, and dynamically adjust the distance parameter D for theparticular intersection. Therefore, for smaller intersections, theparameter D can be reduced so that the algorithm does not predict thepath of the vehicle 354 as turning left until it is closer to theintersection 346, and as such may be past the driveway 356 when thepredicted path feature is initiated. For larger intersections, theparameter D can be maintained or increased to be relatively high becauseof the distance across the intersection 346 where the vehicle 354 wouldstill likely be past the driveway 356.

The last three false positive scenarios discussed herein include afailure to detect stop sign/signal lights by front vision cameras on thehost vehicle (false positive), obstruction of a sensor field-of-view onthe host vehicle by other vehicles, trucks, high mediums, lamp posts,etc. (false negative), and no intersection data being available fromnavigation maps (false negative). These three situations will bediscussed with reference to FIG. 21, which is an illustration 370showing perpendicular roadways 372 and 374 defining an intersection 376,where the roadway 372 includes opposing traffic lanes 378 and 380separated by a center line 382. A host vehicle 384 is traveling in thelane 378 and opposing vehicles 386 are traveling in the lane 380, wherethe host vehicle 384 is turning left into the roadway 374.

Slowing down at an intersection that does not have a stop sign or asignal for the traffic in one of the directions is an indication withoutother vehicles in front of the host vehicle 384 that the host vehicle384 intends to make a left turn. The host vehicle 384 may also slow downat the intersection 376 when there is a stop sign or a signal even whenthe host vehicle 384 is going straight through the intersection 376. Notknowing the intent of the host vehicle 384 becomes even more problematicif there is only a single lane for all of straight through, left-turningand right-turning vehicles. If the algorithm is not able to identify thephase of a traffic signal or a stop sign properly using sensory data,such as a forward vision camera, radar sensors, lidar sensors, V2Xcommunications, etc., a false positive information state can occur.Further, the vehicle driver may not turn on the turn signal showing theintent to turn left. Therefore, the false positive may occur if thealgorithm does not detect the stop sign or signal and the vehicle 384 isslowing down at the intersection 376. In this situation, the algorithmmay be limited to an inform only situation without providing warning orautomatic braking in the absence of additional supporting inputs, suchas a left turn signal, a change in road wheel angle, etc. Pedestriancrossings or road markings may also be used as additional inputs if theyare available. If the intersection 376 is determined to be a stopintersection, where vehicles in the approach region need to stop, thesystem may delay providing the information until additional inputs, suchas turn signal activation or wheel angle, are available.

At an intersection, the field-of-view (FOV) of sensors on the hostvehicle 384 by other vehicles, trucks, high medians, lamp posts, etc.,may cause the sensor to not identify lane markings, and as such, causefalse negatives to be issued. In this situation, the present inventionproposes identifying when such sensor blockages are occurring and informthe driver that the turning assist is in inform mode only. Alternately,V2X information can be used if available.

Sometimes map databases do not have intersection data available, whichcould lead to false negatives. In this situation, if the host vehicle384 is slowing down close to an intersection center and no intersectionis indicated on the map, and without traffic in front of the vehicle384, an indication for a left turn can be provided. The relative motionto other surrounding vehicles can also be used as data. In thissituation, based on the vehicle wheel angle, a predicted path isdetermined that may not be accurate to the intersection 376 since it isnot shown on the map. The algorithm can use yaw rate and past history tocalculate a predicted path when a change in the wheel angle is detected.The turning assist features will use this default predicted path whenthe wheel angle is less than some delta value and use the path historyand yaw rate to calculate the predicted path when the wheel angle isgreater than the delta value to warn the driver.

As will be well understood by those skilled in the art, the several andvarious steps and processes discussed herein to describe the inventionmay be referring to operations performed by a computer, a processor orother electronic calculating device that manipulate and/or transformdata using electrical phenomenon. Those computers and electronic devicesmay employ various volatile and/or non-volatile memories includingnon-transitory computer-readable medium with an executable programstored thereon including various code or executable instructions able tobe performed by the computer or processor, where the memory and/orcomputer-readable medium may include all forms and types of memory andother computer-readable media.

The foregoing discussion discloses and describes merely exemplaryembodiments of the present invention. One skilled in the art willreadily recognize from such discussion and from the accompanyingdrawings and claims that various changes, modifications and variationscan be made therein without departing from the spirit and scope of theinvention as defined in the following claims.

1. A method for collision threat assessment on a host vehicle travelingon a roadway, said method comprising: determining if the host vehicle islikely to turn at or near an intersection with a predetermined level ofconfidence; obtaining velocity and position data of the host vehicleusing sensors onboard the host vehicle; obtaining velocity and positiondata of one or more remote vehicles or other objects at or near theintersection; determining, using a processor in a controller, apredicted path of the host vehicle based on the velocity and positiondata, along with yaw rate or road wheel angle of the host vehicle;determining, using the processor, a predicted path of one or more of theremote vehicles based on the velocity and position data; issuing awarning to a driver of the host vehicle, by the controller, if the hostvehicle and one of the remote vehicles may collide based on thepredicted paths; and providing, using the processor, additionalcollision analysis to reduce false positive warnings and false negativewarnings for specialized host vehicle circumstances.
 2. The methodaccording to claim 1 wherein determining if a host vehicle is likely toturn at or near an intersection includes determining that the hostvehicle is making a lane change.
 3. The method according to claim 2wherein determining that the host vehicle is making a lane changeincludes one or more of: providing data and information from a mapdatabase including number of travel lanes on the roadway, determiningwhether the host vehicle is in an inside lane or an outside lane of theroadway, determining whether opposing traffic is in an adjacent lane toa travel lane of the host vehicle, determining whether same directiontraffic is an adjacent lane to the travel lane of the host vehicle,identifying roadway curbs, identifying lane markings provided by camerason the vehicle, and identifying proximity of the host vehicle to theintersection.
 4. The method according to claim 1 wherein providingadditional collision analysis to reduce false positive warnings andfalse negative warnings includes determining that the host vehicle isentering a left turn lane by identifying type and color of roadway lanemarkings.
 5. The method according to claim 1 wherein providingadditional collision analysis to reduce false positive warnings andfalse negative warnings includes adjusting a distance parameter from theintersection that initiates the collision assessment method so as toreduce false positive warnings as a result of the host vehicle turninginto a driveway before the intersection.
 6. The method according toclaim 1 wherein determining whether the host vehicle is a making a turnat or near the intersection includes determining whether the hostvehicle is slowing down when other vehicles are not in front of the hostvehicle.
 7. The method according to claim 1 wherein providing additionalcollision analysis to reduce false positive warnings and false negativewarnings includes preventing the warning from being issued and providingcollision information only if the host vehicle is unable to detect astop sign or traffic signal at the intersection.
 8. The method accordingto claim 1 wherein providing additional collision analysis to reducefalse positive warnings and false negative warnings includes providinginformation that sensors on the host vehicle have been blocked byobjects when the host vehicle is traveling near the intersection.
 9. Themethod according to claim 1 wherein providing additional collisionanalysis to reduce false positive warnings and false negative warningsincludes identifying type and color of lane markings on the roadway. 10.(canceled)
 11. The method according to claim 1 wherein obtainingvelocity and position data of the host vehicle and the one or moreremote vehicles includes using information and data from one or more ofa map database, a long range radar sensor on the vehicle, a short rangeradar sensor on the vehicle, cameras on the vehicle, lidar sensors onthe vehicle, V2X communications, Internet communications and satellitecommunications.
 12. The method according to claim 1 wherein determiningif the host vehicle is likely to turn at or near an intersectionincludes using information and data from one or more of turn signalactivity, a map database, a long range radar sensor on the vehicle, ashort range radar sensor on the vehicle, cameras on the vehicle, lidarsensors on the vehicle, V2X communications, Internet communications andsatellite communications.
 13. A method for determining whether a hostvehicle traveling on a roadway is turning at an intersection or drivewayor making a lane change, said method comprising: obtaining velocity andposition data of the host vehicle using sensors onboard the hostvehicle; determining, using a processor in a controller, a predictedpath of the host vehicle based on the velocity and position data, alongwith yaw rate or road wheel angle of the host vehicle; and determining,using the processor, whether the host vehicle is turning at theintersection or driveway or making a lane change based on the data andthe predicted path.
 14. The method according to claim 13 whereinobtaining position data of the host vehicle includes determining whetherthe host vehicle is traveling along an inside lane or an outside lane ofthe roadway, and wherein determining whether the host vehicle is turningat the intersection or driveway or making a lane change includesdetermining that the vehicle is turning if the vehicle is traveling inthe inside lane and determining that the vehicle is changing lanes ifthe vehicle is traveling in the outside lane.
 15. The method accordingto claim 14 wherein determining whether the host vehicle is travelingalong an inside lane or an outside lane of the roadway includes usingone or more of a map database, identification of type and color of lanemarkings, identification of roadway curbs, detection of opposing trafficin an adjacent lane, detection of same direction traffic in an adjacentlane, and detection of a center turn lane.
 16. The method according toclaim 13 wherein determining whether the host vehicle is turning at theintersection or driveway or making a lane change includes identifyingtype and color of lane markings on the roadway.
 17. (canceled)
 18. Themethod according to claim 13 wherein obtaining velocity and positiondata of the host vehicle includes using information and data from one ormore of a map database, a long range radar sensor on the vehicle, ashort range radar sensor on the vehicle, cameras on the vehicle, lidarsensors on the vehicle, V2X communications, Internet communications andsatellite communications.
 19. A system for collision threat assessmentfor a host vehicle traveling on a roadway, said system comprising: meansfor determining if the host vehicle is likely to turn at or near anintersection with a predetermined level of confidence; sensors onboardthe host vehicle for obtaining velocity and position data of the hostvehicle; means for obtaining velocity and position data of one or moreremote vehicles or other objects' at or near the intersection; and acontroller including a processor configured with instructions for;determining a predicted path of the host vehicle based on the velocityand position data, along with yaw rate or road wheel angle of the hostvehicle; determining a predicted path of one or more of the remotevehicles based on the velocity and position data; issuing a warning to adriver of the host vehicle if the host vehicle and one of the remotevehicles may collide based on the predicted paths; and providingadditional collision analysis to reduce false positive warnings andfalse negative warnings for specialized host vehicle circumstances. 20.The system according to claim 19 wherein providing additional collisionanalysis to reduce false positive warnings and false negative warningsincludes determining that the host vehicle is entering a left turn laneby identifying type and color of roadway lane markings.